The aim of this study is to evaluate the use of a natural language processing (NLP) software to extract medication statements from unstructured medical discharge letters.
Ten randomly selected discharge letters were extracted from the data warehouse of the University Hospital Erlangen (UHE) and manually annotated to create a gold standard. The AHD NLP tool, provided by MIRACUM's industry partner was used to annotate these discharge letters. Annotations by the NLP tool where then compared to the gold standard on two levels phrase precision (whether or not the whole medication statement has been identified correctly) and token precision (whether or not the medication name has been identified correctly within correctly discovered medication phrases).
The NLP tool detected medication related phrases with an overall F-measure of 0.852. The medication name has been identified correctly with an overall F-measure of 0.936.
This proof-of-concept study is a first step towards an automated scalable evaluation system for MIRACUM's industry partner's NLP tool by using a gold standard. Medication phrases and names have been correctly identified in most cases by the NLP system. Future effort needs to be put into extending and validating the gold standard.
This proof-of-concept study is a first step towards an automated scalable evaluation system for MIRACUM's industry partner's NLP tool by using a gold standard. Medication phrases and names have been correctly identified in most cases by the NLP system. Future effort needs to be put into extending and validating the gold standard.Semantic interoperability is a major challenge in multi-center data sharing projects, a challenge that the German Initiative for Medical Informatics is taking up. With respect to laboratory data, enriching site-specific tests and measurements with LOINC codes appears to be a crucial step in supporting cross-institutional research. However, this effort is very time-consuming, as it requires expert knowledge of local site specifics. To ease this process, we developed a generic manual collaborative terminology mapping tool, the MIRACUM Mapper. It allows the creation of arbitrary mapping workflows involving different user roles. A mapping workflow with two user roles has been implemented at University Hospital Erlangen to support the local LOINC mapping. Additionally, the MIRACUM LabVisualizeR provides summary statistics and visualizations of analyte data. We developed a toolbox that facilitates the collaborative creation of mappings and streamlines the review as well as the validation process. The two tools are available under an open source license.Intraoperative neurophysiological monitoring (IOM) enables a function-preserving surgical strategy for surgeries of brain or spinal cord pathologies by neurophysiological measurements. However, the IOM data management at neurosurgical institutions are often either not digitized or inefficient in terms of collecting, storing and processing of IOM data. Here, we describe the development of a web application, called IOM-Manager, as a first step towards the complete digitization of the IOM workflow. The web application is used for structured protocoling based on standardized protocol entry catalog, data archiving, and data analysis. These functionalities are based on the results of the requirement engineering of a process analysis, a survey with potential users and a market analysis. A usability test with one IOM team indicated the IOM-Manager and its other components can in fact solve many problems of existing solutions.In the field of oncology, a close integration of cancer research and patient care is indispensable. Although an exchange of data between health care providers and other institutions such as cancer registries has already been established in Germany, it does not take advantage of internationally coordinated health data standards. Translational cancer research would also benefit from such standards in the context of secondary data use. This paper employs use cases from the German Cancer Consortium (DKTK) to show how this gap can be closed using a harmonised FHIR-based data model, and how to apply it to an existing federated data platform.Rehabilitation of musculoskeletal diseases (MSD) of the shoulder is a multifaceted long-term process, which is often not transparent to affected patients. Mobile health applications (apps) have the potential to support this complex process by improving patients' self-management skills. However, there seems to be a lack of apps providing a holistic approach to motivate and guide patients during the whole rehabilitation process. Therefore, a systematic analysis of apps on Google Play Store was conducted by two independent reviewers. A total of 3227 apps were identified, of which 64 met the eligibility criteria for the qualitative analysis. The majority of analyzed apps were developed generally for patients with MSD of the shoulder, rarely for specific diseases (individual needs of patients). The majority of apps focus on the provision of information, exercise training, and alternative medicine. https://www.selleckchem.com/products/cinchocaine.html Apps for diagnostics, inpatient treatment, and self-management, especially for multiple rehabilitation phases, are rare or even not existent. Game design elements are seldom used. If there are any, then simple to implement ones, e.g. messages and progress bars. The (psychological) effects of individual game design elements on patients seem to be neglected, when selecting and implementing game-components.The HiGHmed consortium aims to create a shared information governance framework to integrate clinical routine data. One challenge is the replacement of unstructured reporting (e.g. doctoral letters) with structured reporting in clinical routine. The Heidelberg cardiology department evaluates dynamic PDF forms for structured data reporting of heart failure (HF) patients. In this use case, we aim to identify potential caveats or shortcomings in data processing at an early stage. We employed data mining strategies to detect patterns related to incomplete or false data, which we found to be present among all data types. We then discuss the characteristics of the baseline patient cohort in Heidelberg to find out about specific peculiarities and potential biases, which may be site-specific. Briefly, our patient population is predominantly male (67%), NYHA I &amp; II are the most common severity classes, NYHA IV is missing entirely. Most patients have a dilated cardiomyopathy (DCM) or coronary heart disease (CHD) diagnosed as their cause of HF.